This document discusses how utilities can reduce bad debt write-offs through a predictive analytics-based framework. It proposes a three-pronged strategy of identifying high-risk customers, revising collections tactics for those customers, and improving customer interactions. Predictive analytics can help identify high-risk existing and new customers based on various attributes. Utilities can then prioritize collections efforts based on customer risk and debt levels. Improving customer service while following regulations can increase collections rates while maintaining satisfaction. The framework was tested for a utility client and increased debt collections by 50% within 3 months while lowering costs.
The document discusses a user forum for HP Revenue Intelligence that was held in Antalya, Turkey from March 19-20, 2012. It covers topics around fraud and credit risk management, including the relationship between fraud and credit risk systems, credit risk management solutions implemented between 2006-2010, enhancements to the graylist pool, expectations before implementing a new Credit Risk Management System (CRMS) project, highlights and accomplishments of the CRMS project, and examples of credit risk operations with the new system.
The payments landscape has changed significantly and bankers must adapt or be disintermediated by those changes. Check volume will continue to diminish, remote
deposit capture will continue to proliferate, and coin and currency are here to stay.
Online and mobile banking, coupled with increased ATM functionality, will drive consumer banking while non-bank payments and digital wallet services such as Apple Pay are becoming more widely accepted among both consumers and their financial institutions.
New regulations and increased regulatory scrutiny will continue to drive up banks’ costs, while new risks will necessitate improved governance, risk management,
automation, and compliance systems. Banks must re-engineer their commercial deposit products, operations, risk management and cost structures in order to remain competitive and profitable. All of this affects the way that businesses interface with their banks and the costs they bear as customers.
This document provides an overview of PredictiveMetrics' Net30Score model for portfolio and collection scoring. Some key points:
- Net30Score is a statistical model that predicts the probability of customers becoming delinquent or "bad" within 6 months using internal customer data.
- It provides risk classifications and dollars at risk amounts to help prioritize customer accounts and optimize collection strategies.
- Validation results show the model captured nearly 80% of future bad accounts within the highest 30% risk percentile, outperforming random selection.
- The model outputs are delivered through a web application and flat files for integration with other systems to support account management and collections.
1) Identity theft has become one of the fastest growing crimes in the US, representing 36% of fraud complaints in 2006. Credit card and bank fraud made up 41% of identity theft complaints.
2) New regulations under the Fair Credit Reporting Act (FCRA) and Fair and Accurate Credit Transactions Act (FACTA) aim to combat identity theft by requiring financial institutions to implement identity theft prevention programs.
3) The Identity Theft Red Flags and Address Discrepancies rules finalize regulations for financial institutions to identify potential red flags of identity theft, create programs to detect and prevent identity theft, and respond to address discrepancies and changes. Financial institutions must be compliant by November 1, 2008.
The property and casualty insurance industry has seen declining profits in recent years due to lower investment returns and high claims costs. Fraud represents a significant portion of claims costs, estimated at $30 billion annually in the US alone. Predictive analytics can help insurers more efficiently identify fraudulent claims, recover costs through subrogation, optimize staff scheduling, and improve loss reserving. Early adopters of predictive analytics in claims processing are seeing returns of over 100% and improved customer retention compared to companies that have not adopted these techniques.
Regulations are integral to the banking industry, and the extent to which the bank complies with such regulations not just maintains its bottom line in terms of avoiding hefty fines, but also has a big bearing on credibility and integrity. So how do banks comply with all that is required, and save themselves from the ill-effects of non-compliance?
The document discusses a user forum for HP Revenue Intelligence that was held in Antalya, Turkey from March 19-20, 2012. It covers topics around fraud and credit risk management, including the relationship between fraud and credit risk systems, credit risk management solutions implemented between 2006-2010, enhancements to the graylist pool, expectations before implementing a new Credit Risk Management System (CRMS) project, highlights and accomplishments of the CRMS project, and examples of credit risk operations with the new system.
The payments landscape has changed significantly and bankers must adapt or be disintermediated by those changes. Check volume will continue to diminish, remote
deposit capture will continue to proliferate, and coin and currency are here to stay.
Online and mobile banking, coupled with increased ATM functionality, will drive consumer banking while non-bank payments and digital wallet services such as Apple Pay are becoming more widely accepted among both consumers and their financial institutions.
New regulations and increased regulatory scrutiny will continue to drive up banks’ costs, while new risks will necessitate improved governance, risk management,
automation, and compliance systems. Banks must re-engineer their commercial deposit products, operations, risk management and cost structures in order to remain competitive and profitable. All of this affects the way that businesses interface with their banks and the costs they bear as customers.
This document provides an overview of PredictiveMetrics' Net30Score model for portfolio and collection scoring. Some key points:
- Net30Score is a statistical model that predicts the probability of customers becoming delinquent or "bad" within 6 months using internal customer data.
- It provides risk classifications and dollars at risk amounts to help prioritize customer accounts and optimize collection strategies.
- Validation results show the model captured nearly 80% of future bad accounts within the highest 30% risk percentile, outperforming random selection.
- The model outputs are delivered through a web application and flat files for integration with other systems to support account management and collections.
1) Identity theft has become one of the fastest growing crimes in the US, representing 36% of fraud complaints in 2006. Credit card and bank fraud made up 41% of identity theft complaints.
2) New regulations under the Fair Credit Reporting Act (FCRA) and Fair and Accurate Credit Transactions Act (FACTA) aim to combat identity theft by requiring financial institutions to implement identity theft prevention programs.
3) The Identity Theft Red Flags and Address Discrepancies rules finalize regulations for financial institutions to identify potential red flags of identity theft, create programs to detect and prevent identity theft, and respond to address discrepancies and changes. Financial institutions must be compliant by November 1, 2008.
The property and casualty insurance industry has seen declining profits in recent years due to lower investment returns and high claims costs. Fraud represents a significant portion of claims costs, estimated at $30 billion annually in the US alone. Predictive analytics can help insurers more efficiently identify fraudulent claims, recover costs through subrogation, optimize staff scheduling, and improve loss reserving. Early adopters of predictive analytics in claims processing are seeing returns of over 100% and improved customer retention compared to companies that have not adopted these techniques.
Regulations are integral to the banking industry, and the extent to which the bank complies with such regulations not just maintains its bottom line in terms of avoiding hefty fines, but also has a big bearing on credibility and integrity. So how do banks comply with all that is required, and save themselves from the ill-effects of non-compliance?
New credit reporting tools can help companies manage risk and increase revenue. Several newly enhanced specialized credit reporting products are available from various vendors to minimize risk, reduce bad debt, and expand revenue opportunities. It is important to select reports that fully support business objectives and avoid common pitfalls like relying on self-reported data. The quality and depth of data is crucial, and companies should ask questions about data coverage, predictive accuracy, and data sources to ensure reports will provide useful answers.
Fundtech white paper, e invoicing provides new avenues for creditFriso de Jong
1) Electronic invoicing enables new forms of supply chain financing by providing banks visibility into trade transactions and relationships.
2) E-invoicing automates supply chain financing structures like factoring and invoice discounting, improving processes and risk analysis.
3) Integrating e-invoicing with supply chain financing provides opportunities for new financing products focused on payables and receivables.
This white paper discusses how insurers can transform their billing and payment processes from a back office cost center into a competitive differentiator. It argues that billing represents frequent customer touchpoints and impacts key metrics like retention and expenses. The paper recommends developing an enterprise billing strategy to examine how various payment plans and mechanisms affect priorities like fee income, retention, and costs. It suggests implementing a billing service solution to execute the strategy flexibly within 3-5 months while tracking measurable metrics.
A credit score is a numerical representation of a person's creditworthiness based on statistical analysis of their credit files and reports. Lenders use credit scores to evaluate lending risk, determine loan qualifications and pricing, and identify the most profitable customers. Credit scoring analyzes past credit behavior according to the 5 C's - character, capacity, capital, collateral, and conditions - to predict loan repayment likelihood.
Open Banking : The Rise of the Cloud PlatformGary Farrow
This paper explores how traditional banking system architectures will be affected by the emergence of open banking. Platform models for open banking are proposed that accommodate both supply and demand-side solutions. On the demand side, the network effects of open banking platforms and their limitations are discussed.
Accenture-Payments-Regulation-Will-Disrupt-EU-Card-Payment-Ecosystem💡 David Baratta
The document discusses how the revised Payment Services Directive (PSD2) and Interchange Fee Regulation (IFR) are reshaping Europe's payments landscape by driving changes in the card payments ecosystem. PSD2 and IFR will impact acquirers, issuers, and network operators by reducing interchange fees, increasing transparency, and enabling new entrants. While this regulatory change creates uncertainty and risks disintermediation, it also provides opportunities for innovation in payment initiation services, credit and lending solutions, and for network operators to offer new infrastructure services.
Strategies for Payment Systems PlanningGary Farrow
Payments systems modernisation provides one of the most challenging IT planning problems. This article proposes and evaluates a variety of strategies to achieve simplification of a banks payments systems.
BTO BSM for Wholesale Banking circa 2006djasso7494
The document summarizes an HP Business Service Management solution for wholesale payments that helps address challenges facing wholesale banking executives. The solution provides visibility into payment processes, enables customer self-service, improves operational efficiency, and reduces operational risk. It also lays the foundation for long-term transformation towards an enterprise payments strategy. The solution monitors business processes and underlying IT infrastructure from a single console.
The Fair and Accurate Credit Transactions Act of 2003 requires certain financial institutions and creditors to implement an Identity Theft Prevention Program to detect and mitigate identity theft. The Red Flags Rule specifies that programs must include risk assessments, policies to identify and respond to red flags of identity theft, staff training, and oversight of service providers. Failure to comply can result in fines and legal action by regulators and state attorneys general.
Agent banking can significantly reduce the costs of providing financial services to low-income customers compared to traditional branch banking. It does this in two main ways:
1. It minimizes fixed costs by leveraging existing retail outlets rather than requiring investments in new infrastructure. The fixed costs per transaction for branches are much higher than for agent banking.
2. Acquisition costs are lower for mobile-enabled agents and mobile wallets which use mobile phones rather than payment cards. If delivery channels are underutilized, agent banking incurs lower variable costs per transaction since banks only pay commissions for realized transactions.
3. Agent banking may benefit from additional revenue from transactions initiated at the agent location, like transfers, bill payments,
Transaction scoring can help credit card issuers more accurately assess risk and identify opportunities. It does this by analyzing transaction data in real time to identify risky spending patterns or positive credit usage. This allows issuers to intervene earlier with at-risk accounts and guide customers toward responsible credit use. It also reduces "false positives," identifying customers suitable for cross-selling or promotions. Transaction scoring provides benefits to both issuers and consumers by enabling more precise segmentation and tailored account management strategies.
UK Open Banking / Open ID Foundation WorkshopGary Farrow
The document discusses evaluating different authentication flows for Open Banking, including redirection, embedded, and decoupled authentication. It aims to assess each flow's alignment with PSD2 regulations, understand customer experience impacts, and implications for business models. The evaluation will consider authentication journeys across web and mobile applications to provide recommendations to the Implementation Trustee.
Compliance Abhors a Vacuum - If the Void is Filled with Heightened BSA Scruti...CBIZ, Inc.
Bank Secrecy Act (BSA) violations may be the next big regulatory target - and can be very costly. Two cases and takeaways to consider before bank examiners come knocking.
Oracle's Revenue Management and Billing for Financial Services (ORMB) provides banks with a consolidated billing platform to address issues of obtaining a comprehensive customer view, reducing revenue leakage, and eliminating duplicative billing systems. ORMB integrates customer data across lines of business to provide customized pricing and a single invoice for all customer services. It also offers controls and reporting to prevent incorrect pricing and missed charges that can lead to revenue leakage. By consolidating the pricing, billing, and collections processes onto a single platform, ORMB reduces IT costs for banks compared to maintaining separate legacy systems.
Credit Bureau Perspectives for Developing Markets Frank Lenisa
The document discusses considerations for developing credit bureaus in emerging markets based on the experiences of Compuscan, a South African credit bureau. It covers topics such as obtaining data from lenders with limited infrastructure, reporting challenges due to short loan terms and lack of technology, developing credit scoring models with less reliable identifying data, and the need for extensive technical support, training, and public awareness campaigns to establish an effective credit bureau.
Compuscan is a South African company established in 1994 that collects, validates, and distributes consumer credit information. It has 200 employees and offers products and services like credit bureau data, credit scoring, loan management software, collections services, and training. Compuscan also operates in other African countries by building credit bureaus and provides credit reporting.
This document discusses payment factories, which refer to centralized hubs established by companies to gain greater control and efficiency over processing payments. Payment factories can take various forms depending on factors like a company's processes, technology, bank relationships, and location. The document examines the benefits payment factories can provide, such as increased standardization, visibility, control and lower costs. It also provides examples of different payment factory models and considerations for establishing one.
By 1st December 2015, BCBS-IOSCO rules mean that all eligible financial and non-financial counterparties must be able to exchange bilateral Variation Margin (VM) and Initial Margin (IM) with their OTC derivatives counterparties. The consequences of this extend far beyond methodology, requiring a re-evaluation of the whole end to end workflow.
The document provides an overview of the complex Canadian payments system landscape, which involves a wide range of stakeholders. There is significant innovation and choice in payment methods for consumers and businesses. The regulatory environment is also complex, with multiple regulators and government bodies involved. Key trends include the declining use of cheques and growing electronic payments, as well as innovation centered around consumer payments.
New credit reporting tools can help companies manage risk and increase revenue. Several newly enhanced specialized credit reporting products are available from various vendors to minimize risk, reduce bad debt, and expand revenue opportunities. It is important to select reports that fully support business objectives and avoid common pitfalls like relying on self-reported data. The quality and depth of data is crucial, and companies should ask questions about data coverage, predictive accuracy, and data sources to ensure reports will provide useful answers.
Fundtech white paper, e invoicing provides new avenues for creditFriso de Jong
1) Electronic invoicing enables new forms of supply chain financing by providing banks visibility into trade transactions and relationships.
2) E-invoicing automates supply chain financing structures like factoring and invoice discounting, improving processes and risk analysis.
3) Integrating e-invoicing with supply chain financing provides opportunities for new financing products focused on payables and receivables.
This white paper discusses how insurers can transform their billing and payment processes from a back office cost center into a competitive differentiator. It argues that billing represents frequent customer touchpoints and impacts key metrics like retention and expenses. The paper recommends developing an enterprise billing strategy to examine how various payment plans and mechanisms affect priorities like fee income, retention, and costs. It suggests implementing a billing service solution to execute the strategy flexibly within 3-5 months while tracking measurable metrics.
A credit score is a numerical representation of a person's creditworthiness based on statistical analysis of their credit files and reports. Lenders use credit scores to evaluate lending risk, determine loan qualifications and pricing, and identify the most profitable customers. Credit scoring analyzes past credit behavior according to the 5 C's - character, capacity, capital, collateral, and conditions - to predict loan repayment likelihood.
Open Banking : The Rise of the Cloud PlatformGary Farrow
This paper explores how traditional banking system architectures will be affected by the emergence of open banking. Platform models for open banking are proposed that accommodate both supply and demand-side solutions. On the demand side, the network effects of open banking platforms and their limitations are discussed.
Accenture-Payments-Regulation-Will-Disrupt-EU-Card-Payment-Ecosystem💡 David Baratta
The document discusses how the revised Payment Services Directive (PSD2) and Interchange Fee Regulation (IFR) are reshaping Europe's payments landscape by driving changes in the card payments ecosystem. PSD2 and IFR will impact acquirers, issuers, and network operators by reducing interchange fees, increasing transparency, and enabling new entrants. While this regulatory change creates uncertainty and risks disintermediation, it also provides opportunities for innovation in payment initiation services, credit and lending solutions, and for network operators to offer new infrastructure services.
Strategies for Payment Systems PlanningGary Farrow
Payments systems modernisation provides one of the most challenging IT planning problems. This article proposes and evaluates a variety of strategies to achieve simplification of a banks payments systems.
BTO BSM for Wholesale Banking circa 2006djasso7494
The document summarizes an HP Business Service Management solution for wholesale payments that helps address challenges facing wholesale banking executives. The solution provides visibility into payment processes, enables customer self-service, improves operational efficiency, and reduces operational risk. It also lays the foundation for long-term transformation towards an enterprise payments strategy. The solution monitors business processes and underlying IT infrastructure from a single console.
The Fair and Accurate Credit Transactions Act of 2003 requires certain financial institutions and creditors to implement an Identity Theft Prevention Program to detect and mitigate identity theft. The Red Flags Rule specifies that programs must include risk assessments, policies to identify and respond to red flags of identity theft, staff training, and oversight of service providers. Failure to comply can result in fines and legal action by regulators and state attorneys general.
Agent banking can significantly reduce the costs of providing financial services to low-income customers compared to traditional branch banking. It does this in two main ways:
1. It minimizes fixed costs by leveraging existing retail outlets rather than requiring investments in new infrastructure. The fixed costs per transaction for branches are much higher than for agent banking.
2. Acquisition costs are lower for mobile-enabled agents and mobile wallets which use mobile phones rather than payment cards. If delivery channels are underutilized, agent banking incurs lower variable costs per transaction since banks only pay commissions for realized transactions.
3. Agent banking may benefit from additional revenue from transactions initiated at the agent location, like transfers, bill payments,
Transaction scoring can help credit card issuers more accurately assess risk and identify opportunities. It does this by analyzing transaction data in real time to identify risky spending patterns or positive credit usage. This allows issuers to intervene earlier with at-risk accounts and guide customers toward responsible credit use. It also reduces "false positives," identifying customers suitable for cross-selling or promotions. Transaction scoring provides benefits to both issuers and consumers by enabling more precise segmentation and tailored account management strategies.
UK Open Banking / Open ID Foundation WorkshopGary Farrow
The document discusses evaluating different authentication flows for Open Banking, including redirection, embedded, and decoupled authentication. It aims to assess each flow's alignment with PSD2 regulations, understand customer experience impacts, and implications for business models. The evaluation will consider authentication journeys across web and mobile applications to provide recommendations to the Implementation Trustee.
Compliance Abhors a Vacuum - If the Void is Filled with Heightened BSA Scruti...CBIZ, Inc.
Bank Secrecy Act (BSA) violations may be the next big regulatory target - and can be very costly. Two cases and takeaways to consider before bank examiners come knocking.
Oracle's Revenue Management and Billing for Financial Services (ORMB) provides banks with a consolidated billing platform to address issues of obtaining a comprehensive customer view, reducing revenue leakage, and eliminating duplicative billing systems. ORMB integrates customer data across lines of business to provide customized pricing and a single invoice for all customer services. It also offers controls and reporting to prevent incorrect pricing and missed charges that can lead to revenue leakage. By consolidating the pricing, billing, and collections processes onto a single platform, ORMB reduces IT costs for banks compared to maintaining separate legacy systems.
Credit Bureau Perspectives for Developing Markets Frank Lenisa
The document discusses considerations for developing credit bureaus in emerging markets based on the experiences of Compuscan, a South African credit bureau. It covers topics such as obtaining data from lenders with limited infrastructure, reporting challenges due to short loan terms and lack of technology, developing credit scoring models with less reliable identifying data, and the need for extensive technical support, training, and public awareness campaigns to establish an effective credit bureau.
Compuscan is a South African company established in 1994 that collects, validates, and distributes consumer credit information. It has 200 employees and offers products and services like credit bureau data, credit scoring, loan management software, collections services, and training. Compuscan also operates in other African countries by building credit bureaus and provides credit reporting.
This document discusses payment factories, which refer to centralized hubs established by companies to gain greater control and efficiency over processing payments. Payment factories can take various forms depending on factors like a company's processes, technology, bank relationships, and location. The document examines the benefits payment factories can provide, such as increased standardization, visibility, control and lower costs. It also provides examples of different payment factory models and considerations for establishing one.
By 1st December 2015, BCBS-IOSCO rules mean that all eligible financial and non-financial counterparties must be able to exchange bilateral Variation Margin (VM) and Initial Margin (IM) with their OTC derivatives counterparties. The consequences of this extend far beyond methodology, requiring a re-evaluation of the whole end to end workflow.
The document provides an overview of the complex Canadian payments system landscape, which involves a wide range of stakeholders. There is significant innovation and choice in payment methods for consumers and businesses. The regulatory environment is also complex, with multiple regulators and government bodies involved. Key trends include the declining use of cheques and growing electronic payments, as well as innovation centered around consumer payments.
Role of Analytics in Delivering Health Information to help fight Cancer in Au...Deanna Kosaraju
Voices 2014
Role of Analytics in Delivering Health Information to help fight Cancer in Australia
Katerina Andronis,
Deloitte Consulting, Australia and Chandana Unnithan,
Deakin University, Australia
DistribuTECH 2016: OMNETRIC next generation outage managementOMNETRIC
The OMNETRIC Group joined Accenture Utilities at DistribuTECH 2016 in Orlando to give demonstrations to attendees on our next generation outage management solutions.
European Utility Week 2015: Data Analytics and Wind Power ForecastingOMNETRIC
The document discusses the growing importance of wind power in Europe and the challenges of integrating intermittent wind generation. It notes that better wind power forecasting is key to managing wind power and achieving renewable energy targets. More accurate forecasting can be achieved through combining multiple forecast models and leveraging additional weather and power output data. This "smart combination" approach allows utilities to better utilize their existing forecasting solutions.
Ormita offers a way for businesses to collect bad debts over 120 days old by having debtors pay in goods or services instead of cash. The creditor receives advertising, products, or other goods from Ormita equal to the debt value, avoiding the costs of litigation. Debtors benefit by resolving debts with surplus stock instead of cash. Creditors avoid loss of value from unpaid bills or repossessing assets, while debtors resolve debts without borrowing cash. The process involves lodging the debt with Ormita, who contacts the debtor to arrange transferring goods in exchange for settling the debt.
This document provides an overview of accounting concepts related to bad debt, allowances for doubtful debt, and discounts. It defines bad debt and doubtful debt, and explains how to record journal entries for bad debt expenses, bad debt recovered, and adjustments to allowance for doubtful debt. It also defines discount allowable and explains how to estimate and record allowance for discount allowable. The goal is to help the reader understand how to account for amounts that may not be fully collected from debtors.
From grid infrastructure analytics to consumer analytics, the true power of data is starting to be realized. Greentech Media Co-Founder and President, Rick Thompson, sets the stage for the days presentations and panels.
What are big data in the contacts of energy & utilities, and how/where can the utilities find value in the data. In this C-level presentation we discussed the three prime areas: grid operations, smart metering and asset & workforce management. A section on cognitive computing for utilities have been omitted from the presentation due to confidentiality - but I tell you - it is mind-blowing perspectives on how IBM Watson will help utilities plan and optimize their operations in the near future!
See more on http://www.ibmbigdatahub.com/industry/energy-utilities
With flickery markets, edgy economy, organizational change and the evolving regulatory landscape, the finance divisions are caught up in a fast increase in the amount of public support and changes. All this while, the need for cost cutting and delivering transparent reports stays stable. Rolta’s Financial Analytics solution CFO Impact helps you bring cost effective and sustainable transformations to financial processes and systems with the help of big data analytic technologies.
1. Analytics is increasingly important in the banking industry for applications like risk management, fraud detection, and customer segmentation. Tools like data mining and predictive analytics help banks understand customer behavior and mitigate risks.
2. Analytics supports decision making to increase revenue, reduce costs, and manage risks. This improves customer retention and understanding. Popular analytics tools in banking include R, SAS, and Python.
3. Use cases for banking analytics include customer analytics, fraud analysis, big data analytics, and risk analytics. Analytics provides insights into areas like marketing, compliance, and optimal performance.
K-score models are statistical tools that estimate the probability of default or likelihood that a customer will repay debts. Common models include FICO and Vantage, which assign scores ranging from 300 to 850 based on factors like payment history, credit utilization, and types of credit used. While K-scores can help lenders standardize decisions and improve risk assessment, they also have disadvantages like not accounting for new users, potential data errors influencing scores, and the risk of manipulation. Overall, statistical credit scoring aids lending accuracy and efficiency but also comes with challenges to consider.
PredictiveMetrics (PMI) is an analytics firm that develops statistical predictive models to help clients in collections, recovery, and debt buying/selling. It has a team of statisticians and analysts with proprietary software and tools. PMI specializes in leveraging clients' internal data like accounts receivable and placement data to develop customized predictive scores. These scores help clients optimize collections, reduce costs and delinquencies, and maximize profits. PMI has various consumer and commercial scoring products and provides ongoing services to clients.
Automation and Analytics: Two Levers to Revitalize Retail Debt RecoveryCognizant
As retail banks strive to revive, they can deploy predictive analytics and other process automation tools to add efficiency and effectiveness to the debt recovery process, thereby increasing recovery rates, reducing costs and enhancing debt salability.
WNS’ commercial banking solutions coupled with cutting-edge transformational solutions enable superior customer experience & cost-effective commercial banking operations.
Get more details on - https://s3.wns.com/S3_5/Documents/Articles/PDFFiles/7064/274/3_Step_Changes_That_Transform_Commercial_Credit_Appraisal.pdf
Credit Audit's Use of Data Analytics in Examining Consumer Loan PortfoliosJacob Kosoff
Written by Jacob Kosoff and published in September 2013 by the RMA Journal. This article describes banks in 2012 & 2013 were modernizing their Credit Review functions.
The document discusses challenges facing insurance carriers in improving claims operations, including declining revenues, increased competition, and changing customer behaviors. It suggests that carriers can address these challenges by targeting high-volume, low premium policies without compromising claims service through various means, such as using technology to lower costs while improving customer satisfaction and retention. Emerging markets present additional unique challenges for claims processing that some carriers are addressing by automating processes and leveraging new technologies without legacy system constraints.
White Paper: From Accounts Receivable to Smarter ReceivablesMoretonSmith
This paper sets-out MoretonSmith’s Smarter Receivables concept and describes how it can be pursued to implement the optimum balance of people, process and technology, in order to achieve transformational insights, efficiency and effectiveness in accounts receivable.
Most business owners fail to understand that, just as there is a human life cycle, there is also a business life cycle. It begins with the conceptual or idea stage, which progresses to the start-up period, followed by the growth phase, then the well-established maturing years,and finally the exit or retirement phase. most common mistake most business owners make is that they don’t plan far enough in advance for the exit phase.
INTEGRATION OF MACHINE LEARNING TECHNIQUES TO EVALUATE DYNAMIC CUSTOMER SEGME...IJDKP
The telecommunications industry is highly competitive, which means that the mobile providers need a
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
The document discusses the future of payments in the 21st century and how new technologies and business models are disrupting traditional payment systems. It analyzes trends like real-time payments, use of unique identifiers like phone numbers and emails, push-based systems like PayPal versus pull-based card networks, improved security and fraud controls, lower processing costs, and the transition away from paper checks and plastic cards to digital and mobile-based payments. PayPal is highlighted as an example of a company leveraging these 21st century innovations to build a highly successful new payments platform.
Whitepaper - Leveraging Analytics to build collection strategiesArup Das
1) Financial institutions can leverage predictive analytics to create dynamic collections strategies that maximize recovery amounts at each stage of the collections lifecycle.
2) Early collections focus on identifying high-risk customers to target for early recovery, while allowing low-risk customers time to self-cure. Late and recovery stages use harder tactics like legal notices on high-risk defaulters most likely to default further.
3) By considering both probability of recovery and expected recovery amount, institutions can prioritize accounts and strategies to boost profits from collections.
Retail Banking 6 Steps to Improving the Collections Experience.pptxMaveric Systems
The recent global slowdown, policy overlays, debt moratoriums, fiscal injections, and increased
delinquency rates have all brought the scanner on collections experience. Today’s credit environment is different, and so are the digital-first customer support systems and the banks’ criticality to maintaining long-term customer loyalty.
Retail Banking 6 Steps to Improving the Collections Experience.pdfMaveric Systems
Undoubtedly, the next-generation collections model in retail banking must involve a nuanced understanding of the at-risk customers and the corresponding interventions. A digital-first collections strategy will shoulder the bulk of this burden. The numbers, too, bear this out. A study points out that digital customer service reduces 20% in non-performing loans (NPL), resolves 30+ past due dates (PDD) by 25%, reduces 15% in collections cost, and boosts customer engagement by 5X.
Retail Banking 6 Steps to Improving the Collections Experience.pdfMaveric Systems
The recent global slowdown, policy overlays, debt moratoriums, fiscal injections, and increased delinquency rates have all brought the scanner on collections experience. Today’s credit environment is different, and so are the digital-first customer support systems and the banks’ criticality to maintaining long-term customer loyalty.
Blog-Embryonic Opportunities For Predictive Analytics In AustraliaArup Das
Australia has introduced comprehensive credit reporting (CCR) which allows lenders to share positive consumer credit information. This new form of reporting, coupled with predictive analytics, can help lenders improve risk management and customer service. CCR involves sharing additional positive data on consumers' accounts and repayment history that provides insights into customers' risk appetites, loyalty, and financial well-being. This enhanced data set enables more powerful customer analysis, risk assessments, and tailored product offerings to benefit both lenders and consumers.
Similar to How a Predictive Analytics-based Framework Helps Reduce Bad Debts in Utilities (20)
• • With the evolving role of the CFOs, businesses more than ever are now reliant on their CFOs to translate mere numbers into incisive intelligence to tap new growth opportunities
• The right insights can prime the finance function to swiftly respond to market volatility, facilitate strategic expansion, spot trends, accurately predict outcomes, and proactively drive top-line and bottom-line growth
• CFOs can also make an impact at the larger enterprise level by incorporating analytics to add value outside it core functions and help other functions
• Given the pre-dominant importance of technology and the role it plays as a Big Data enabler, it is imperative for CFOs to forge collaborative and strategic relationships with CIOs
Civil aviation authorities across the world are becoming increasingly stringent in penalizing airlines for flight delays and disruptions.
As recently as in August 2014, the Civil Aviation Administration of China (CAAC) announced a six-month crackdown on flight disruptions. Domestic airlines will receive internal warnings if their on-time flight rankings are in the bottom 20 of all airlines, or if flight delays occur more than half the time. In the case of delays or poor service that trigger serious protests by passengers, airlines risk losing all their flight slots in the current season and will lose the right to apply for flight slots in the next season.
Strategies for Optimizing Baggage Handling to Increase Customer LoyaltyWNS Global Services
The document discusses the challenges airlines face with mishandled baggage and damaged customer relationships. It explores how airlines can leverage technology and outsourcing to third-party partners to more effectively track baggage and proactively manage customer service issues. The key points are that baggage issues are a major cause of customer dissatisfaction, technology can help with tracking but not relationships, and outsourcing integrated baggage and customer service solutions can significantly reduce complaints and improve efficiency.
How to Find Solutions for Strategic Concerns in HR through AnalyticsWNS Global Services
This document summarizes insights from a Twitter chat discussing how HR professionals are using analytics to address strategic concerns. Top concerns included ROI on workforce investment, quality of new hires, change management, workforce planning, and retaining talent. HR professionals are increasingly adopting descriptive, predictive, and prescriptive analytics using tools and frameworks to align workforce performance with business strategy and address talent gaps. Analytics allows showing the relationship between HR and business metrics and calculating true ROI, helping make insight-driven strategic decisions. Advancements in technology and successful examples are driving more organizations to adopt HR analytics.
This document provides an overview of the challenges facing the utilities sector in the UK. It discusses the intense political pressure over affordability and security of supply that the energy and water industries are facing. The utilities sector believes the current political climate is threatening its ability to secure necessary investment and that regulation has become more susceptible to political pressure. However, the utilities sector also recognizes opportunities to restore public trust by improving customer service, communications, and responsiveness to issues. The overview indicates the sectors will need to balance priorities of reducing carbon emissions, ensuring security of supply, and keeping prices affordable.
WNS (Holdings) Ltd. owns the copyright to this document from 2014. The document discusses WNS (Holdings) Ltd. and some of its business services such as finance and accounting, procurement, and customer interaction services. It also mentions that WNS utilizes proprietary tools and methodologies to deliver these outsourcing services to its clients around the world.
4 Smart Options to Overcome the Lonely Journey to Captive Shared ServicesWNS Global Services
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2. Bad Debt Write-offs — Business
Trade-off or Survival Tactic?
For the past few years, utilities have relinquished
hundreds of thousands of dollars in consumer bad
debts. Customer defaults continue to rise in an
environment speckled with rising levels of
unemployment, economic uncertainty and dipping
consumer spends. A spate of stringent
government regulations — to protect customer
rights, reduce environmental impact and improve
safety compliance — do not make it any easier
for the utilities business to thrive. To make
matters worse, unscrupulous consumers continue
to exploit loopholes in the utility's business
processes to default on their payments.
Bad debts force utilities to trade off profits for
survival. When towing the line between bad
debts, failed collections efforts and a stringent
regulatory environment, utilities are forced to take
the 'write-off' route even if it means giving up on
the revenue they rightly owed.
Little wonder then, that, write-offs have risen
from approximately USD 400 Million in 2008 to
about USD 2.8 Billion in 2014, as reported by a
leading strategy consultancy firm, PA Consulting,
in its recent customer service benchmarking data.
In such an environment laden with constraints,
how can your utility company effectively minimize
bad debt write-offs?
This whitepaper puts forth the answers to this
critical question.
Utilities can reduce their bad debts significantly
by adopting the 'integrated three-pronged revenue
protection strategy' that focuses on:
n Identifying high-risk customers;
n Revising collections tactics targeted towards
the high-risk customer segment; and
n Improving customer interactions and
experience interventions.
WNSExtending Your Enterprise
Fig.1: The typical maze of challenges that utilities have to grapple with.
01 wns.com
Unscrupulous
Customers
Stringent Govt.
Regulations
Economic
Uncertainty
Rising Levels of
Unmemployment
3. The customer occupies a central position in this
strategy — analyzing, understanding and
predicting customer behavior becomes central to
its success. The level of customer understanding,
required for this three-pronged framework, is
enabled only by predictive analytics. Predictive
analytics is an advanced form of data analytics
that utilizes a large number of variables based on
both internal and external data sources and
leverages advanced statistical tools as well as
specialized analytical techniques to predict likely
future outcomes.
Predictive analytics lays the foundation to this
strategy by helping identify high-risk customer
behavior and in enabling the implementation of
collections strategies targeted towards high-risk
customer segments.
Predictive Analytics for
Identifying High-risk
Customer Behavior
With the risk of bad debts looming large, utility
companies cannot afford to follow a one-scheme-
fits-all policy for managing customer defaults.
Most utilities charge their customers on a 'credit'
basis, that is, after the use of the service.
Reliance on credit payment is not ideal for all
customer categories, as customers tend to misuse
this option. Utilities should first make efforts to
identify and classify customers (both existing and
new) into high- and low-risk segments and then
develop targeted strategies to securitize revenue
from high-risk customer segments.
Fig.2: Predictive analytics lays the ground for an effective bad debt minimization strategy.
02wns.com
PREDICTIVE ANALYSIS
PREDICTIVEANALYSIS
Enhanced Customer
Satisfaction Interventions
Targeted Collection
Strategies
Identifying High-Risk
Customers
How a Predictive Analytics-based
Framework Helps Reduce Bad Debts in Utilities
4. Predictive analytical models that assess risks
during the onboarding of new customers use
profile parameters such as income levels,
demographics, and credit history. Most utilities
have stringent SOPs for evaluating new customer
applications; however, they often overlook risks
lurking within existing customer accounts. Risks
in existing customer accounts can be identified
by analyzing additional information, such as,
customer meter settings, usage patterns, payment
history, and complaints and communication.
WNSExtending Your Enterprise
03
NEW CUSTOMER
n Income Levels
n Demographics
n Credit History
EXISTING CUSTOMER
n Customer Meter Settings
n Usage Patterns
n Payment History
n Complaints and Communication
Fig. 3: Profile parameters to identify risks vary between new and existing customer accounts.
Customers, who find it difficult to pay their utility
dues, usually request for negotiation of payment
terms and credit extensions from their utility
providers. However, there are instances where
customers default even after such options are
provided and may opt for unscrupulous practices
to escape payment. Some may pose as 'new'
customers and apply to the utility company for a
new account, while some may move to new
addresses frequently, without informing their
utility suppliers. Although most utility companies
ask for information on the account holder's name,
the Social Security number, and / or tax ID,
individuals resorting to the 'name game' conceal
these bits of information that can prove their
links to other accounts. Utility companies that
fail to identify customers with prior trailing dues,
run into the cycle of customer defaults, bad debts
and the resultant losses.
Predictive data analytics helps identify fraudulent
customers that have 'trailing' debts and may
resort to 'name game' tactics to get away without
paying their dues. The segment of potential
defaulters can be further expanded by including
more parameters such as customer profile and
credit rating data from credit bureaus namely
Equifax, Experian, and TransUnion. Identifying
a larger number of customers under the ambit
of potential defaulters further minimizes the risk
of delinquencies.
wns.com
5. Vendors with expertise in data management (data
collection, cleaning, preparation and analysis)
can effectively assist utility companies prevent
revenue leakage by spotting aliases and
customers with high attrition risk.
A proven predictive analytics model is one that
allows utilities to segment customers based on
two parameters — the debt value the customer
owes, and the propensity to pay back the debt.
By plotting the outstanding dues on the x-axis
and the propensity to pay back the debt on the
y-axis, utilities can create a collections
prioritization matrix (as shown in fig. 4) to decide
on the next steps in the collections strategy.
04
Outstanding Dues
PropensitytoPayBack
Low
Do Nothing
Minimal Collections
Efforts
Minimal Collections
Efforts
Medium
Do Nothing
Steady Follow-up
Strict Follow-up
High
Steady Follow-up
Strict Follow-up
Accelerated Collection
Efforts
LowMediumHigh
Fig.4: Sample collections prioritization matrix prescribing 'collections' steps to be taken.
This novel approach in segmentation on the basis
of the customer's propensity to pay back the debt
provides new insight for the debt collection
strategy. As shown in fig. 4, customer accounts
with high outstanding and propensity to pay are
prioritized for accelerated in-house collections.
On the other hand, a lower outstanding amount
and propensity to pay accounts are written-off
immediately as the effort and cost-to-collect the
debt, exceeds the debt due on the customer
amount.
Proactive action upon identification of high-risk
customers can help reduce bad debt write-offs by
as much as 40 percent and securitize revenues
for utility providers.
wns.com
How a Predictive Analytics-based
Framework Helps Reduce Bad Debts in Utilities
6. Predictive Analytics-driven
Technology for Revamping
Collections Tactics
An efficient collections strategy aims to improve
the collections rate and minimize the cost- and
effort-to-collect. The collections process follows a
'dunning path' during which the utility company
follows up the customer to pay up the
outstanding amount. If such efforts fail or end in
no recovery after a certain period of time, the
customer account is placed under a Debt
Collection Agency (DCA). The average recovery
rates for DCAs vary within 10-15 percent across
primary, secondary and tertiary placements,
depending on the region. The average
commissions are in the range of 25-50 percent,
based on the age of the debt. In scenarios where
all debt accounts are transferred to DCAs,
collection costs tend to become exorbitant. Thus,
most utility companies have an internal debt
management and collections team focused on
early-stage delinquent accounts.
Most collections focused contact centers deploy
state-of-the-art information technology and
telephony infrastructure that help them improve
outbound contact center performance.
The two most commonly deployed forms of
automation in a collections contact center are
the 'Predictive Dialer' and 'Computer Telephony
Integration' (CTI).
Predictive dialers that operate on the principles
of predictive analytics, measure the number of
available agents, available lines, and average call
handling time to improve resource utilization.
The predictive dialer has the capability to
automatically call a list of telephone numbers in
sequence, screening out no-answers, busy
signals, answering machines and disconnected
numbers while predicting possible points at
which a human caller will be able to handle the
next call.
CTI in turn, links the dialer with the customer
information system to disseminate customer
account information to collection agents. It
essentially displays the propensity rank (based on
analytical modeling) of the customer along with
the collections strategy, helping agents, tailor
their conversation, offer advice and re-structure
payment methods, as shown in fig. 5.
05
WNSExtending Your Enterprise
HighMediumLow
Outstanding Dues (in £)
High Medium Low
PropensitytoPayBack
HM HL
MM ML
LM
MH
LH LL
HH
Collection Tips:
n High Value Customer - Deal Respectfully
n Recover Full Outstanding - Do not offer part settlement
until debt > 3000K on the customer account
n Maximum repayment tenure is 60 months
n Retain customer
Fig.5: A sample CTI screenshot, showing collection tips generated for the collections agent.
wns.com
7. One very effective way to check payment
delinquencies is to employ a cut-off credit score
for onboarding new customers.
Many utility companies set this threshold limit at
low levels to ensure that marginal consumers
continue to receive their services without needing
to pay a deposit. By simply raising this limit,
utilities can increase the number of customers
that are required to pay a deposit. Though
consumer protection regulations vary between
states in the UK, nearly all states allow utility
companies to ask for a security deposit from
high-risk customers.
The deposit amount can be customized to suit
customized specific customer demographics
without harming the revenue-generating potential
of the utility company. For instance, utility
companies can determine the deposit amount
based on the tariff structure, payment track
records, and the disconnection history of a
customer. Pre-payment meters offer an effective
option to check debt pile-up on a delinquent
customer account.
Traditionally, utilities follow the route of
converting customer accounts in debt to pre-
payment meters only after the account exceeds a
certain debt threshold value or if it remains in
debt for long. A smarter strategy would be to
proactively promote pre-paid meters to the
identified segment of high risk customers before
the first instance of default occurs. This strategy
helps protect revenue and reduces the resources
and efforts spent on debt collections.
A combination of these tweaks and changes leads
to improved collections performance and reduced
bad debt on customer accounts.
The first two steps of this revenue protection
framework focus on predictive analytics tools,
technology and models. The third step
concentrates on improving customer interactions
and experience and works in tandem with the
first two steps.
Improving Customer
Interactions and Experience
For most consumers, paying off utility bills,
figures as the last of their 'payment priorities', in
comparison with insurance, video-DTH rentals,
and telephone bills. Since, electricity, gas and
water are considered essential life services, utility
companies cannot disconnect services to
customers who have not paid their bills, for
recovery of debts.
Despite the pressing need to recover revenue
from defaulting customers, utility providers need
to be mindful of customer satisfaction and
experience. After all, customer satisfaction lays
the foundation to retain market share amidst
increasing competition.
Maneuvering between a rigorous collections
strategy and ensuring a high customer
satisfaction index at the same time can be tricky.
However, it is not impossible.
A blend of appropriate advisor knowledge, utility
collections experience, empathy towards
customers in financial distress and compliance to
consumer protection regulations can increase
collections success and at the same time create a
base of satisfied customers.
A knowledgeable customer service advisor has the
right orientation needed to understand the
customer and industry dynamics. This orientation
comes from rigorous trainings on evaluation of
the customer's situation and the ability to show
empathy by offering financial advice or flexible
repayment modes to suit the customer's profile.
With such interventions customers usually open
up to the prospect of discussing different
payment options before entering into a promise to
pay back the debt. This improves customer
commitment to honor agreed payment schedules
and reduces effort in the collections process.
06wns.com
How a Predictive Analytics-based
Framework Helps Reduce Bad Debts in Utilities
8. Further, as utilities operate in a regulated
environment, they need to comply with consumer
protection guidelines of the state. These
regulations pertain to customer interaction, mode
of collecting debt, identification of inability to
pay, treatment of customers on social tariffs, and
protection of vulnerable customers. Non-
compliance invites harsh penalties. Utilities are
responsible for the actions of DCAs who collect
the debt from customers on their behalf. In
recent times, there has an increase in the number
of complaints to the Office of Gas and Electricity
Markets (OFGEM) about aggressive debt
collection practices. This puts additional pressure
on utilities to re-evaluate their current debt
collection mechanism and put in place a system
to govern DCA performance.
It is advisable to strike a balance between
proactive collection steps to address customer
delinquency and adherence to consumer
protection regulations.
Optimizing the debt collection process shows
significant positive results within a short period of
implementing a pilot process based on the above
discussed three-point framework. Consumer
delinquency management methods have the
potential to reduce losses incurred by utility
companies by as much 50 percent. What's more,
the payoff is much higher than investment and
sustainable in the long run.
07
WNSExtending Your Enterprise
wns.com
9. The Client
The Challenge
The WNS Solution
A Leadin
The client wanted to better manage its energy
final debt portfolio. Its debt recovery rate was at
4 percent, compared to the 14 percent achieved
by its competitors. On the other hand, the
commissions charged by the client's debt
collection agencies were as high as 50 percent of
the collected amount that kept the operational
cost-to-collect very high. This made dents in the
profit margins of the client. The client wanted to
optimize its final debt collection processes to
improve recovery of receivables. The client also
wanted to formulate focused debt management
strategies for different customer segments to
manage customer write-offs more effectively and
in the process decrease operational costs.
WNS concentrated on transforming the client's
collections process by embedding
e customer
interaction strategy. Key aspects of the
WNS solution were:
n Propensity-to-Pay predictive data model
exclusively for residential customers. This
model predicted the likelihood of customers
being able to pay their dues after their
accounts were finalized. The model assigned a
propensity-to-pay score to every customer.
n Customer classification into high, medium, and
low propensity-to-pay segments based on
their scores.
n Focused delinquency management strategies
for every segment.
g Energy and Utilities Company
predictive
analytics and making changes to th
n Customer segment prioritization for outbound
contact. Customer segments were prioritized on
the basis of the propensity-to-pay scores and
the amount of outstanding debt.
n Rigorous cost-benefit analysis to streamline
operational, financial, and human resource
activities. This exercise would go on to
optimize the debt management process.
n Inbound and outbound test strategy
implementation to engage customers.
The customer service executives used the
customized call scripts and pre-determined
verbiage to carry out settlement negotiations
with and provide debt management advice
to customers.
n Performance monitoring of pilot strategies
regularly against critical tactical and quality
indicators and also against the parameters set
by the champion process.
By using predictive analytics to carry out
propensity based customer segmentation and
enforcing customized engagement and advisory
policies for different customer segments, WNS
was able to fulfill the business objectives of the
client. The WNS solution achieved the following
results for the client:
n Debt collection increased by 50 percent within
3 months
n The challenger process recorded an 8 percent
rise in conversion rates compared to the
champion process
n Operational expenses decreased by 20 percent
Thus WNS helped the client optimize its
collections process by bringing in actionable
insights using predictive analytics.
Benefits Delivered
08
How One of the Leading Utility Companies
Increased its Debt Collections by 50 Percent
in 3 Months with Predictive Analytics
wns.com
How a Predictive Analytics-based
Framework Helps Reduce Bad Debts in Utilities
10. Financial
Benefits
Reduction in Days Sales Outstanding
Increase in Top-Line Revenue
Improvement in Credit Ratings
Compliance with Regulatory Guidelines
Improvement in Customer Relations
and Market Standing
Reputation
Management
Defining
Context for
Growth
Aligning
Business
Objectives
Embedding
Analytics
Designing
Strategies
Operationalizing
Process
Excellence
Knowledge
Management
Collection
Revenue
Promoter Index
Continuous Improvement
through Innovation
Cost to
Collect Broken
Promises
Gathering Business Intelligence
Defining Program Statement
Building Productive
models and Visualizations
Drawing data driven
conclusions
Analytics based tailor-made process
improvements (based on customer
segmentation and risk score)
Real-time model assessment by
champion / challenger strategies
End-to-end service architecture to
achieve business outcomes
Periodic model treatment
for relevancy
Learning best practices
from diverse industry
experience
Impact assessment
Reduce customer effort by
streamlining processes
ROI projection
Fig. 6: The framework used by WNS to transform the client's collections process
Figure 7: The Benefits of Reducing Consumer Bad Debt Write-Offs in the Utility Industry
09
WNSExtending Your Enterprise
wns.com
11. 10wns.com
Conclusion
Reducing consumer bad debt write-offs has
several benefits in the utility industry. These
benefits have a positive effect on the day-to-day
operations of the company and also impact its
reputation in the market along with
stakeholder relations.
Utility companies need to reduce instances and
volumes of consumer bad debt write-offs to stay
competitive in a dynamic economic environment.
What's more, they need to achieve this goal in the
face of continuing challenges—shrinking income
levels of consumers, stringent regulatory
guidelines, and pressure from shareholders to
optimize performance and returns on investment.
Predictive analytics has emerged as a key enabler
that helps segment customers into identical
groups based on their attributes and formulate
customized debt management and advisory
policies targeted to a particular
customer segment.
Targeted strategies assure better performance and
acceptance with the customer segment and helps
reduce consumer bad debt write-offs and drive
several significant business benefits, by
improving customer satisfaction levels.
How a Predictive Analytics-based
Framework Helps Reduce Bad Debts in Utilities